[official] PyTorch implementation of TimeVQVAE from the paper ["Vector Quantized Time Series Generation with a Bidirectional Prior Model", AISTATS 2023]

[PVLDB 2024 Best Paper Nomination] TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods

Non-Metric Space Library (NMSLIB): An efficient similarity search library and a toolkit for evaluation of k-NN methods for generic non-metric spaces.

DuckDB is an analytical in-process SQL database management system

Pytorch implementation of SOM-VAE: INTERPRETABLE DISCRETE REPRESENTATION LEARNING ON TIME SERIES https://arxiv.org/pdf/1806.02199v7.pdf

A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputatio…

Python implementation of k-Shape

code for CIVET

KDD21 Deep Learning Embeddings for Data Series Similarity Search

PyTorch Tutorial for Deep Learning Researchers

Python plotting library of alps-lab style using matplotlib.

Official repository for the paper “Learned Data-aware Image Representations of Line Charts for Similarity Search” (SIGMOD'23)

LLM inference in C/C++

Dimensionality reduction in very large datasets using Siamese Networks

Public development project of the LAMMPS MD software package

MiniAero Unstructured Finite Volume Compressible Navier-Stokes Mini-App

American Fuzzy Lop fork server and instrumentation for pure-Python code

Code for the ICML 2023 paper "Geometric Autoencoders - What You See is What You Decode"

Code for the ICML 2023 paper "Geometric Autoencoders - What You See is What You Decode"

Time Series Representation Models

Python port of R's Comprehensive Dynamic Time Warp algorithms package

Geometry Regularized Autoencoders (GRAE) for large-scale visualization and manifold learning

Topologically Regularized Data Embeddings

Code for the paper "Topological Autoencoders" by Michael Moor, Max Horn, Bastian Rieck, and Karsten Borgwardt.

This repository contains code to the paper "Manifold based time series forecasting"